Pyu2f is a Python-based U2F host library. It provides functionality for interacting with a U2F device over USB.

This is a python wrapper for the curve25519 library with ed25519 signatures. The C code was pulled from libaxolotl-android. At the moment this wrapper is meant for use by python-axolotl.

MCUboot is a secure bootloader for 32-bit MCUs. This package provides a tool to securely sign firmware images for booting by MCUboot.

The Stateless OpenPGP Command-Line Interface (or sop) is a specification that encourages OpenPGP implementors to provide a common, relatively simple command-line API for purposes of object security.

This Python module helps implementers build such a CLI from any implementation accessible to the Python interpreter.

It does not provide such an implementation itself -- this is just the scaffolding for the command line, which should make it relatively easy to supply a handful of python functions as methods to a class.

This package provides another Scrypt module for Python.

This library handles the low-level details of NTLM authentication for use in authenticating with a service that uses NTLM. It will create and parse the 3 different message types in the order required and produce a base64 encoded value that can be attached to the HTTP header.

The goal of this library is to offer full NTLM support including signing and sealing of messages as well as supporting MIC for message integrity and the ability to customise and set limits on the messages sent. Please see Features and Backlog for a list of what is and is not currently supported.

OMEMO cryptography library that was forked from python-axolotl.

This package contains test vectors for the cryptography package.

This package provides Python bindings for the Rust crate of blake3, a cryptographic hash function.

PyOTP is a Python library for generating and verifying one-time passwords. It can be used to implement two-factor (2FA) or multi-factor (MFA) authentication methods in web applications and in other systems that require users to log in.

This package provides async versions of Kivy functions to avoid the callback-heavy mode of interaction typical in some Kivy applications.

This is the Cython-coded accelerator module for PyOpenGL.

This package provides support for async/await applications without requiring an event loop, useful for creative responsive GUIs.

This package provides Python bindings for GLFW OpenGL application development library.

VisPy is a high-performance interactive 2D/3D data visualization library leveraging the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets.

Kivy is a software library for rapid development of hardware-accelerated multitouch applications.

Pyglet is a Python library for developing games and other visually-rich applications. It supports windowing, user interface event handling, Joysticks, OpenGL graphics, loading images and videos, playing sounds and music.

This package provides Kivy widgets that approximate Google's Material Design spec without sacrificing ease of use or application performance.

Python-glcontext is a library providing an OpenGL implementation for ModernGL on multiple platforms.

Pyglet is a Python library for developing games and other visually-rich applications. It supports windowing, user interface event handling, Joysticks, OpenGL graphics, loading images and videos, playing sounds and music.

Pivy provides python bindings for Coin, a 3D graphics library with an Application Programming Interface based on the Open Inventor 2.1 API.

PyOpenGL is the most common cross platform Python binding to OpenGL and related APIs. The binding is created using the standard ctypes library.

PySDL2 is a pure Python wrapper around the SDL2,SDL2_mixer, SDL2_image, SDL2_ttf, and SDL2_gfx libraries. Instead of relying on C code, it uses the built-in ctypes module to interface with SDL2, and provides simple Python classes and wrappers for common SDL2 functionality.

python-pandera provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust. Dataframes contain information that python-pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. With python-pandera, you can:

• Define a schema once and use it to validate different dataframe types.

• Check the types and properties of columns.

• Perform more complex statistical validation like hypothesis testing.

• Seamlessly integrate with existing data pipelines via function decorators.

• Define dataframe models with the class-based API with pydantic-style syntax.

• Synthesize data from schema objects for property-based testing.

• Lazily validate dataframes so that all validation rules are executed.

• Integrate with a rich ecosystem of tools like python-pydantic, python-fastapi and python-mypy.